BackgroundElectronic health records (EHRs) are important data resources for clinical studies and applications. Physicians or clinicians describe patients’ disorders or treatment procedures in EHRs using free text (unstructured) clinical notes. The narrative information plays an important role in patient treatment and clinical research. However, it is challenging to make machines understand the clinical narratives.ObjectiveThis study aimed to automatically identify Chinese clinical entities from free text in EHRs and make machines semantically understand diagnoses, tests, body parts, symptoms, treatments, and so on.MethodsThe dataset we used for this study is the benchmark dataset with human annotated Chinese EHRs, released by the China Conference on Knowledge Graph and Semantic Computing 2017 clinical named entity recognition challenge task. Overall, 2 machine learning models, the conditional random fields (CRF) method and bidirectional long short-term memory (LSTM)-CRF, were applied to recognize clinical entities from Chinese EHR data. To train the CRF–based model, we selected features such as bag of Chinese characters, part-of-speech tags, character types, and the position of characters. For the bidirectional LSTM-CRF–based model, character embeddings and segmentation information were used as features. In addition, we also employed a dictionary-based approach as the baseline for the purpose of performance evaluation. Precision, recall, and the harmonic average of precision and recall (F1 score) were used to evaluate the performance of the methods.ResultsExperiments on the test set showed that our methods were able to automatically identify types of Chinese clinical entities such as diagnosis, test, symptom, body part, and treatment simultaneously. With regard to overall performance, CRF and bidirectional LSTM-CRF achieved a precision of 0.9203 and 0.9112, recall of 0.8709 and 0.8974, and F1 score of 0.8949 and 0.9043, respectively. The results also indicated that our methods performed well in recognizing each type of clinical entity, in which the “symptom” type achieved the best F1 score of over 0.96. Moreover, as the number of features increased, the F1 score of the CRF model increased from 0.8547 to 0.8949.ConclusionsIn this study, we employed two computational methods to simultaneously identify types of Chinese clinical entities from free text in EHRs. With training, these methods can effectively identify various types of clinical entities (eg, symptom and treatment) with high accuracy. The deep learning model, bidirectional LSTM-CRF, can achieve better performance than the CRF model with little feature engineering. This study contributed to translating human-readable health information into machine-readable information.

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Background: Electronic health records (EHR) is an important data resource for clinical studies and applications. Physicians or clinicians describe patients' disorders or treatment procedures using free texts in EHR. The narrative information play an important role in patient treatment and clinical research. However, it is challenging to made machine understand the clinical narratives. Objective: This study aimed to automatically identify Chinese clinical entities from free texts in EHR, and make machine semantically understand diagnosis, test, body part, symptom, treatment and, etc. Methods: Two machine learning (ML) models, conditional random fields (CRF) method and bidirectional LSTM-CRF, were applied to recognize clinical entities from Chinese EHR data. For training the CRF-based model, we selected features as bag of Chinese characters, part-of-speech (POS) tags, character types and the position of characters. For the bidirectional LSTM-CRF-based model, character embeddings and segmentation information were used as features. In addition, we used a dictionarybased approach as the baseline for performance evaluation purpose. Results: To validate our methods, we used the benchmark data set with human annotated Chinese electronic health records, released by CCKS 2017 CNER challenge task. The result showed that our methods were able to automatically identify types of Chinese clinical entities such as diagnosis, test, symptom body part and treatment in one-round running. The identification overall performance of CRF and bidirectional LSTM-CRF achieved Precision of 0.9203 and 0.9112, Recall of 0.8709 and 0.8974, F1 score of 0.8949 and 0.9043 respectively. The result also indicated that our methods performed well on recognizing each type of clinical entities, in which the "symptom" type achieved the best with F1 score over 0.96. Conclusions: In this study, we developed two computational methods to simultaneously identify types of Chinese clinical entities from free texts in EHRs. Via training, it can effectively identify various types of clinical entities (e.g., symptom and treatment) with high accuracy. This study contributed to translating humanreadable health information into machine-readable one.

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